



"""simple docstring"""

import collections
import inspect
import unittest

from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
							from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class     lowerCAmelCase__						:


	'''simple docstring'''







	def __init__(      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       List[str]=13     ,       _SCREAMING_SNAKE_CASE				:       Dict=32     ,       _SCREAMING_SNAKE_CASE				:       Tuple=2     ,       _SCREAMING_SNAKE_CASE				:       Dict=3     ,       _SCREAMING_SNAKE_CASE				:       List[str]=16     ,       _SCREAMING_SNAKE_CASE				:       Dict=[1, 2, 1]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=[2, 2, 4]     ,       _SCREAMING_SNAKE_CASE				:       Any=2     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=2.0     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=True     ,       _SCREAMING_SNAKE_CASE				:       int=0.0     ,       _SCREAMING_SNAKE_CASE				:       Any=0.0     ,       _SCREAMING_SNAKE_CASE				:       int=0.1     ,       _SCREAMING_SNAKE_CASE				:       int="gelu"     ,       _SCREAMING_SNAKE_CASE				:       int=False     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True     ,       _SCREAMING_SNAKE_CASE				:       List[str]=0.0_2     ,       _SCREAMING_SNAKE_CASE				:       Dict=1E-5     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=True     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=None     ,       _SCREAMING_SNAKE_CASE				:       str=True     ,       _SCREAMING_SNAKE_CASE				:       str=10     ,       _SCREAMING_SNAKE_CASE				:       int=8     ,       )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     parent
					SCREAMING_SNAKE_CASE						:							List[Any]							=     batch_size
					SCREAMING_SNAKE_CASE						:							int							=     image_size
					SCREAMING_SNAKE_CASE						:							Any							=     patch_size
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     num_channels
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     embed_dim
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     depths
					SCREAMING_SNAKE_CASE						:							List[Any]							=     num_heads
					SCREAMING_SNAKE_CASE						:							Tuple							=     window_size
					SCREAMING_SNAKE_CASE						:							Dict							=     mlp_ratio
					SCREAMING_SNAKE_CASE						:							Any							=     qkv_bias
					SCREAMING_SNAKE_CASE						:							List[str]							=     hidden_dropout_prob
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     drop_path_rate
					SCREAMING_SNAKE_CASE						:							Any							=     hidden_act
					SCREAMING_SNAKE_CASE						:							int							=     use_absolute_embeddings
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     patch_norm
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     layer_norm_eps
					SCREAMING_SNAKE_CASE						:							Dict							=     initializer_range
					SCREAMING_SNAKE_CASE						:							Any							=     is_training
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     scope
					SCREAMING_SNAKE_CASE						:							Any							=     use_labels
					SCREAMING_SNAKE_CASE						:							Tuple							=     type_sequence_label_size
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     encoder_stride





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Dict							=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     None
					if self.use_labels:
									SCREAMING_SNAKE_CASE						:							int							=     ids_tensor([self.batch_size]     ,       self.type_sequence_label_size    )

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.get_config()

					return config, pixel_values, labels





	def 			_lowerCAmelCase     (      self				:       str    )						->	str:





					"""simple docstring"""


					return SwinvaConfig(
					    image_size=self.image_size     ,       patch_size=self.patch_size     ,       num_channels=self.num_channels     ,       embed_dim=self.embed_dim     ,       depths=self.depths     ,       num_heads=self.num_heads     ,       window_size=self.window_size     ,       mlp_ratio=self.mlp_ratio     ,       qkv_bias=self.qkv_bias     ,       hidden_dropout_prob=self.hidden_dropout_prob     ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,       drop_path_rate=self.drop_path_rate     ,       hidden_act=self.hidden_act     ,       use_absolute_embeddings=self.use_absolute_embeddings     ,       path_norm=self.patch_norm     ,       layer_norm_eps=self.layer_norm_eps     ,       initializer_range=self.initializer_range     ,       encoder_stride=self.encoder_stride     ,       )





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Dict    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     SwinvaModel(config=_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()
					SCREAMING_SNAKE_CASE						:							List[str]							=     model(_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							str							=     ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths    ) - 1))
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     int(config.embed_dim * 2 ** (len(config.depths    ) - 1)    )

					self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, expected_seq_len, expected_dim)    )





	def 			_lowerCAmelCase     (      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       List[str]    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     SwinvaForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()
					SCREAMING_SNAKE_CASE						:							Tuple							=     model(_SCREAMING_SNAKE_CASE    )
					self.parent.assertEqual(
					    result.logits.shape     ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

					# test greyscale images
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     1
					SCREAMING_SNAKE_CASE						:							int							=     SwinvaForMaskedImageModeling(_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()

					SCREAMING_SNAKE_CASE						:							int							=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
					SCREAMING_SNAKE_CASE						:							Any							=     model(_SCREAMING_SNAKE_CASE    )
					self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, 1, self.image_size, self.image_size)    )





	def 			_lowerCAmelCase     (      self				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       int    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.type_sequence_label_size
					SCREAMING_SNAKE_CASE						:							Any							=     SwinvaForImageClassification(_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     model(_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE    )
					self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)    )







	def 			_lowerCAmelCase     (      self				:       Any    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     self.prepare_config_and_inputs()
					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     config_and_inputs
					SCREAMING_SNAKE_CASE						:							Any							=     {'pixel_values': pixel_values}
					return config, inputs_dict




@require_torch
class     lowerCAmelCase__						(	_UpperCAmelCase							,							_UpperCAmelCase							,							unittest.TestCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= (
	    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
	)
	_SCREAMING_SNAKE_CASE	:		Any  						= (
	    {'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	_SCREAMING_SNAKE_CASE	:		int  						= False
	_SCREAMING_SNAKE_CASE	:		int  						= False
	_SCREAMING_SNAKE_CASE	:		Optional[Any]  						= False
	_SCREAMING_SNAKE_CASE	:		int  						= False





	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     SwinvaModelTester(self    )
					SCREAMING_SNAKE_CASE						:							List[str]							=     ConfigTester(self     ,       config_class=_SCREAMING_SNAKE_CASE     ,       embed_dim=37    )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Tuple:





					"""simple docstring"""


					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()





	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE    )





	@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'    )
	def 			_lowerCAmelCase     (      self				:       Any    )						->	str:





					"""simple docstring"""


					pass





	@unittest.skip(reason='Swinv2 does not use inputs_embeds'    )
	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	str:





					"""simple docstring"""


					pass





	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[int]							=     self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE						:							int							=     model_class(_SCREAMING_SNAKE_CASE    )
									self.assertIsInstance(model.get_input_embeddings()     ,       (nn.Module)    )
									SCREAMING_SNAKE_CASE						:							str							=     model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE     ,       nn.Linear    )    )





	def 			_lowerCAmelCase     (      self				:       Tuple    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Tuple							=     self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     model_class(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     inspect.signature(model.forward    )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     [*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     ['pixel_values']
									self.assertListEqual(arg_names[:1]     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       List[str]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Dict							=     self.model_tester.prepare_config_and_inputs_for_common()
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     True

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     True
									SCREAMING_SNAKE_CASE						:							Tuple							=     False
									SCREAMING_SNAKE_CASE						:							List[Any]							=     True
									SCREAMING_SNAKE_CASE						:							Dict							=     model_class(_SCREAMING_SNAKE_CASE    )
									model.to(_SCREAMING_SNAKE_CASE    )
									model.eval()
									with torch.no_grad():
													SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    )
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     outputs.attentions
									SCREAMING_SNAKE_CASE						:							List[str]							=     len(self.model_tester.depths    )
									self.assertEqual(len(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE    )

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									SCREAMING_SNAKE_CASE						:							List[str]							=     True
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     config.window_size**2
									SCREAMING_SNAKE_CASE						:							int							=     model_class(_SCREAMING_SNAKE_CASE    )
									model.to(_SCREAMING_SNAKE_CASE    )
									model.eval()
									with torch.no_grad():
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    )
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     outputs.attentions
									self.assertEqual(len(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE    )

									self.assertListEqual(
									    list(attentions[0].shape[-3:]    )     ,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,       )
									SCREAMING_SNAKE_CASE						:							Dict							=     len(_SCREAMING_SNAKE_CASE    )

									# Check attention is always last and order is fine
									SCREAMING_SNAKE_CASE						:							Any							=     True
									SCREAMING_SNAKE_CASE						:							str							=     True
									SCREAMING_SNAKE_CASE						:							Dict							=     model_class(_SCREAMING_SNAKE_CASE    )
									model.to(_SCREAMING_SNAKE_CASE    )
									model.eval()
									with torch.no_grad():
													SCREAMING_SNAKE_CASE						:							Optional[int]							=     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    )

									if hasattr(self.model_tester     ,       'num_hidden_states_types'    ):
													SCREAMING_SNAKE_CASE						:							List[Any]							=     self.model_tester.num_hidden_states_types
									else:
													# also another +1 for reshaped_hidden_states
													SCREAMING_SNAKE_CASE						:							int							=     2
									self.assertEqual(out_len + added_hidden_states     ,       len(_SCREAMING_SNAKE_CASE    )    )

									SCREAMING_SNAKE_CASE						:							Tuple							=     outputs.attentions

									self.assertEqual(len(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE    )

									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]    )     ,       [self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,       )





	def 			_lowerCAmelCase     (      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       str    )						->	List[str]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     model_class(_SCREAMING_SNAKE_CASE    )
					model.to(_SCREAMING_SNAKE_CASE    )
					model.eval()

					with torch.no_grad():
									SCREAMING_SNAKE_CASE						:							Tuple							=     model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )    )

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     outputs.hidden_states

					SCREAMING_SNAKE_CASE						:							Dict							=     getattr(
					    self.model_tester     ,       'expected_num_hidden_layers'     ,       len(self.model_tester.depths    ) + 1    )
					self.assertEqual(len(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE    )

					# Swinv2 has a different seq_length
					SCREAMING_SNAKE_CASE						:							List[Any]							=     (
					    config.patch_size
					    if isinstance(config.patch_size     ,       collections.abc.Iterable    )
					    else (config.patch_size, config.patch_size)
					)

					SCREAMING_SNAKE_CASE						:							Dict							=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

					self.assertListEqual(
					    list(hidden_states[0].shape[-2:]    )     ,       [num_patches, self.model_tester.embed_dim]     ,       )

					SCREAMING_SNAKE_CASE						:							int							=     outputs.reshaped_hidden_states
					self.assertEqual(len(_SCREAMING_SNAKE_CASE    )     ,       _SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Dict							=     reshaped_hidden_states[0].shape
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     (
					    reshaped_hidden_states[0].view(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       height * width    ).permute(0     ,       2     ,       1    )
					)
					self.assertListEqual(
					    list(reshaped_hidden_states.shape[-2:]    )     ,       [num_patches, self.model_tester.embed_dim]     ,       )





	def 			_lowerCAmelCase     (      self				:       str    )						->	Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.model_tester.prepare_config_and_inputs_for_common()

					SCREAMING_SNAKE_CASE						:							Any							=     (
					    self.model_tester.image_size
					    if isinstance(self.model_tester.image_size     ,       collections.abc.Iterable    )
					    else (self.model_tester.image_size, self.model_tester.image_size)
					)

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE						:							str							=     True
									self.check_hidden_states_output(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									SCREAMING_SNAKE_CASE						:							int							=     True

									self.check_hidden_states_output(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Any							=     self.model_tester.prepare_config_and_inputs_for_common()
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     3

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     (
					    self.model_tester.image_size
					    if isinstance(self.model_tester.image_size     ,       collections.abc.Iterable    )
					    else (self.model_tester.image_size, self.model_tester.image_size)
					)
					SCREAMING_SNAKE_CASE						:							List[Any]							=     (
					    config.patch_size
					    if isinstance(config.patch_size     ,       collections.abc.Iterable    )
					    else (config.patch_size, config.patch_size)
					)

					SCREAMING_SNAKE_CASE						:							int							=     image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
					SCREAMING_SNAKE_CASE						:							Tuple							=     image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE						:							Tuple							=     True
									self.check_hidden_states_output(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       (padded_height, padded_width)    )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									SCREAMING_SNAKE_CASE						:							List[str]							=     True
									self.check_hidden_states_output(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       (padded_height, padded_width)    )





	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Dict							=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Any    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE    )





	@slow
	def 			_lowerCAmelCase     (      self				:       Optional[int]    )						->	Any:





					"""simple docstring"""


					for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE						:							List[str]							=     SwinvaModel.from_pretrained(_SCREAMING_SNAKE_CASE    )
									self.assertIsNotNone(_SCREAMING_SNAKE_CASE    )







	def 			_lowerCAmelCase     (      self				:       str    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							str							=     self.model_tester.prepare_config_and_inputs_for_common()

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     _config_zero_init(_SCREAMING_SNAKE_CASE    )
					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE						:							Any							=     model_class(config=_SCREAMING_SNAKE_CASE    )
									for name, param in model.named_parameters():
													if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
																	self.assertIn(
																	    ((param.data.mean() * 1E9).round() / 1E9).item()     ,       [0.0, 1.0]     ,       msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""     ,       )




@require_vision
@require_torch
class     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	@cached_property
	def 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	Any:





					"""simple docstring"""


					return (
					    AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'    )
					    if is_vision_available()
					    else None
					)







	@slow
	def 			_lowerCAmelCase     (      self				:       Optional[Any]    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'    ).to(
					    _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     self.default_image_processor

					SCREAMING_SNAKE_CASE						:							Tuple							=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
					SCREAMING_SNAKE_CASE						:							str							=     image_processor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors='pt'    ).to(_SCREAMING_SNAKE_CASE    )

					# forward pass
					with torch.no_grad():
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     model(**_SCREAMING_SNAKE_CASE    )

					# verify the logits
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.Size((1, 1_000)    )
					self.assertEqual(outputs.logits.shape     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							int							=     torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6]    ).to(_SCREAMING_SNAKE_CASE    )
					self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,       _SCREAMING_SNAKE_CASE     ,       atol=1E-4    )    )












"""simple docstring"""





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


SCREAMING_SNAKE_CASE__   =  get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
       SCREAMING_SNAKE_CASE__   =  json.load(f)

@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            return FSMTTokenizer.from_pretrained(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			FSMTForConditionalGeneration.from_pretrained(lowercase					).to(lowercase					)
            if torch_device == "cuda":
                  model.half()
            return model





      @parameterized.expand(
          [
              ["""en-ru""", 26.0],
              ["""ru-en""", 22.0],
              ["""en-de""", 22.0],
              ["""de-en""", 29.0],
          ]					)
      @slow
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Dict:
            # note: this test is not testing the best performance since it only evals a small batch
            # but it should be enough to detect a regression in the output quality
            lowerCAmelCase    		=			f'facebook/wmt19-{pair}'
            lowerCAmelCase    		=			self.get_tokenizer(lowercase					)
            lowerCAmelCase    		=			self.get_model(lowercase					)

            lowerCAmelCase    		=			bleu_data[pair]["""src"""]
            lowerCAmelCase    		=			bleu_data[pair]["""tgt"""]

            lowerCAmelCase    		=			tokenizer(lowercase ,		return_tensors="""pt""" ,		truncation=lowercase ,		padding="""longest"""					).to(lowercase					)
            lowerCAmelCase    		=			model.generate(
                input_ids=batch.input_ids ,		num_beams=8 ,		)
            lowerCAmelCase    		=			tokenizer.batch_decode(
                lowercase ,		skip_special_tokens=lowercase ,		clean_up_tokenization_spaces=lowercase					)
            lowerCAmelCase    		=			calculate_bleu(lowercase ,		lowercase					)
            print(lowercase					)
            self.assertGreaterEqual(scores["""bleu"""] ,		lowercase					)






